An update of the J48Consolidated WEKA’s class: CTC algorithm enhanced with the notion of coverage

This document aims to describe an update of the implementation of the J48Consolidated class within WEKA platform. The J48Consolidated class implements the CTC algorithm [2][3] which builds a unique decision tree based on a set of samples. The J48Consolidated class extends WEKA’s J48 class which implements the well-known C4.5 algorithm. This implementation was described in the technical report "J48Consolidated: An implementation of CTC algorithm for WEKA". The main, but not only, change in this update is the integration of the notion of coverage in order to determine the number of samples to be generated to build a consolidated tree. We define coverage as the percentage of examples of the training sample present in –or covered by– the set of generated subsamples. So, depending on the type of samples that we use, we will need more or less samples in order to achieve a specific value of coverage.

Measurements of resistance and reactance in fish with the use of bioelectrical impedance analysis: sources of error

New technologies can be riddled with unforeseen sources of error, jeopardizing the validity and application of their advancement. Bioelectrical impedance analysis (BIA) is a new technology in fisheries research that is capable of estimating proximate composition, condition, and energy content in fish quickly, cheaply, and (after calibration) without the need to sacrifice fish. Before BIA can be widely accepted in fisheries science, it is necessary to identify sources of error and determine a means to minimize potential errors with this analysis. We conducted controlled laboratory experiments to identify sources of errors within BIA measurements. We concluded that electrode needle location, procedure deviations, user experience, time after death, and temperature can affect resistance and reactance measurements. Sensitivity analyses showed that errors in predictive estimates of composition can be large (>50%) when these errors are experienced. Adherence to a strict protocol can help avoid these sources of error and provide BIA estimates that are both accurate and precise in a field or laboratory setting.

Movement patterns of winter flounder (Pseudopleuronectes americanus) in the southern Gulf of Maine: observations with the use of passive acoustic telemetry

Despite its recreational and commercial importance, the movement patterns and spawning habitats of winter flounder (Pseudopleuronectes americanus) in the Gulf of Maine are poorly understood. To address these uncertainties, 72 adult winter flounder (27–48 cm) were fitted with acoustic transmitters and tracked by passive telemetry in the southern Gulf of Maine between 2007 and 2009. Two sympatric contingents of adult winter flounder were observed, which exhibited divergent spawning migrations. One contingent remained in coastal waters during the spawning season, while a smaller contingent of winter flounder was observed migrating to estuarine habitats. Estuarine residence times were highly variable, and ranged from 2 to 91 days (mean=28 days). Flounder were nearly absent from the estuary during the fall and winter months and were most abundant in the estuary from late spring to early summer. The observed seasonal movements appeared to be strongly related to water temperature. This is the first study to investigate the seasonal distribution, migration, and spawning behavior of adult winter flounder in the Gulf of Maine by using passive acoustic telemetry. This approach offered valuable insight into the life history of this species in nearshore and estuarine habitats and improved the information available for the conservation and management of this species.

Dividing population genetic distance data with the software Partitioning Optimization with Restricted Growth Strings (PORGS): an application for Chinook salmon (Oncorhynchus tshawytscha), Vancouver Island, British Columbia

A new method of finding the optimal group membership and number of groupings to partition population genetic distance data is presented. The software program Partitioning Optimization with Restricted Growth Strings (PORGS), visits all possible set partitions and deems acceptable partitions to be those that reduce mean intracluster distance. The optimal number of groups is determined with the gap statistic which compares PORGS results with a reference distribution. The PORGS method was validated by a simulated data set with a known distribution. For efficiency, where values of n were larger, restricted growth strings (RGS) were used to bipartition populations during a nested search (bi-PORGS). Bi-PORGS was applied to a set of genetic data from 18 Chinook salmon (Oncorhynchus tshawytscha) populations from the west coast of Vancouver Island. The optimal grouping of these populations corresponded to four geographic locations: 1) Quatsino Sound, 2) Nootka Sound, 3) Clayoquot +Barkley sounds, and 4) southwest Vancouver Island. However, assignment of populations to groups did not strictly reflect the geographical divisions; fish of Barkley Sound origin that had strayed into the Gold River and close genetic similarity between transferred and donor populations meant groupings crossed geographic boundaries. Overall, stock structure determined by this partitioning method was similar to that determined by the unweighted pair-group method with arithmetic averages (UPGMA), an agglomerative clustering algorithm.

Validation of back-calculation equations for juvenile bluefish (Pomatomus saltatrix) with the use of tetracycline-marked otoliths

In recent years, a decrease in the abundance of bluefish (Pomatomus saltatrix) has been observed (Fahay et al., 1999; Munch and Conover, 2000) that has led to increased interest in a better understanding the life history of the species. Estimates of several young-of-the-year (YOY) life history characteristics, including the importance and use of estuaries as nursery habitat (Kendall and Walford, 1979) and size-dependant mortality (Hare and Cowen, 1997), are reliant upon the accuracy of growth determination. By using otoliths, it is possible to use back-calculation formulae (BCFs) to estimate the length at certain ages and stages of development for many species of fishes. Use of otoliths to estimate growth in this way can provide the same information as long-term laboratory experiments and tagging studies without the time and expense of rearing or recapturing fish. The difficulty in using otoliths in this way lies in validating that 1) there is constancy in the periodicity of the increment formation, and 2) there is no uncoupling of the relationship between somatic and otolith growth. To date there are no validation studies demonstrating the relationship between otolith growth and somatic growth for bluefish. Daily increment formation in otoliths has been documented for larval (Hare and Cowen, 1994) and juvenile bluefish (Nyman and Conover, 1988). Hare and Cowen (1995) found ageindependent variability in the ratio of otolith size to body length in early age bluefish, although these differences varied between ontogenetic stages. Furthermore, there have been no studies where an evaluation of back-calculation methods has been combined with a validation of otolithderived lengths for juvenile bluefish.

Genetic Variants in MiRNA Processing Genes and Pre-MiRNAs Are Associated with the Risk of Chronic Lymphocytic Leukemia

Genome wide association studies (GWAS) have identified several low-penetrance susceptibility alleles in chronic lymphocytic leukemia (CLL). Nevertheless, these studies scarcely study regions that are implicated in non-coding molecules such as microRNAs (miRNAs). Abnormalities in miRNAs, as altered expression patterns and mutations, have been described in CLL, suggesting their implication in the development of the disease. Genetic variations in miRNAs can affect levels of miRNA expression if present in pre-miRNAs and in miRNA biogenesis genes or alter miRNA function if present in both target mRNA and miRNA sequences. Therefore, the present study aimed to evaluate whether polymorphisms in pre-miRNAs, and/or miRNA processing genes contribute to predisposition for CLL. A total of 91 SNPs in 107 CLL patients and 350 cancer-free controls were successfully analyzed using TaqMan Open Array technology. We found nine statistically significant associations with CLL risk after FDR correction, seven in miRNA processing genes (rs3805500 and rs6877842 in DROSHA, rs1057035 in DICER1, rs17676986 in SND1, rs9611280 in TNRC6B, rs784567 in TRBP and rs11866002 in CNOT1) and two in pre-miRNAs (rs11614913 in miR196a2 and rs2114358 in miR1206). These findings suggest that polymorphisms in genes involved in miRNAs biogenesis pathway as well as in pre-miRNAs contribute to the risk of CLL. Large-scale studies are needed to validate the current findings.